Computation Offloading In Mobile Cloud Research Articles

Smart Resource Allocation in Mobile Cloud Next-Generation Network (NGN) Orchestration with Context-Aware Data and Machine Learning for the Cost Optimization of Microservice Applications

Mobile cloud computing (MCC) provides resources to users to handle smart mobile applications. In MCC, task scheduling is the solution for mobile users’ context-aware computation resource-rich applications. Most existing approaches have achieved a moderate service reliability rate due to a lack of instance-centric resource estimations and task offloading, a statistical NP-hard problem. The current intelligent scheduling process cannot address NP-hard problems due to traditional task offloading approaches. To address this problem, the authors design an efficient context-aware service offloading approach based on instance-centric measurements. The revised machine learning model/algorithm employs task adaptation to make decisions regarding task offloading. The proposed MCVS scheduling algorithm predicts the usage rates of individual microservices for a practical task scheduling scheme, considering mobile device time, cost, network, location, and central processing unit (CPU) power to train data. One notable feature of the microservice software architecture is its capacity to facilitate the scalability, flexibility, and independent deployment of individual components. A series of simulation results show the efficiency of the proposed technique based on offloading, CPU usage, and execution time metrics. The experimental results efficiently show the learning rate in training and testing in comparison with existing approaches, showing efficient training and task offloading phases. The proposed system has lower costs and uses less energy to offload microservices in MCC. Graphical results are presented to define the effectiveness of the proposed model. For a service arrival rate of 80%, the proposed model achieves an average 4.5% service offloading rate and 0.18% CPU usage rate compared with state-of-the-art approaches. The proposed method demonstrates efficiency in terms of cost and energy savings for microservice offloading in mobile cloud computing (MCC).

A Survey on Methods and Apparatus of Offloading in Mobile Cloud Computing

A Survey on Methods and Apparatus of Offloading in Mobile Cloud Computing

A survey of research on computation offloading in mobile cloud computing

A survey of research on computation offloading in mobile cloud computing

Towards Data and Computation Offloading in Mobile Cloud Computing: Taxonomy, Overview, and Future Directions

The rapid developments in the mobile application context illuminate the demand for more resources and processing power at Smart Mobile Devices (SMDs). Mobile Cloud Computing (MCC) enables the SMDs to offload their workloads on the remote cloud servers and benefit from the MCC’s extensive resources to deal with this issue. To this end, numerous offloading schemes are provided in the literature to enhance the SMD's efficiency by offloading their workloads on the nearby cloudlets or remote cloud computing resources. This article puts forward a comprehensive survey and taxonomy of the offloading approaches designed and proposed for MCCs. It first classifies them based on the algorithms which have been used for making the offloading decisions. Then, in each category, it illuminates how the offloading decisions are made to improve application performance and mobile devices' energy efficiency, regarding offloading factors such as deadlines, costs, etc. The evaluation metrics, simulator, offloading type, and architecture of the studied schemes are compared and illuminated in each category. Furthermore, regarding the various properties of the studied offloading methods, the offloading domain's leading issues and challenges are discussed. Lastly, the concluding points are provided, and directions for the subsequent studies in the offloading context are specified.

EFFORT: Energy efficient framework for offload communication in mobile cloud computing

SummaryThere is an abundant expansion in the race of technology, specifically in the production of data, because of the smart devices, such as mobile phones, smart cards, sensors, and Internet of Things (IoT). Smart phones and devices have undergone an enormous evolution in a way that they can be used. More and more new applications, such as face recognition, augmented reality, online interactive gaming, and natural language processing are emerging and attracting the users. Such applications are generally data intensive or compute intensive, which demands high resource and energy consumption. Mobile devices are known for the resource scarcity, having limited computational power and battery life. The tension between compute/data intensive application and resource constrained mobile devices hinders the successful adaption of emerging paradigms. In the said perspective, the objective of this article is to study the role of computation offloading in mobile cloud computing to supplement mobile platforms ability in executing complex applications. This article proposes a systematic approach (EFFORT) for offload communication in the cloud. The proposed approach provides a promising solution to partially solve energy consumption issue for communication‐intensive applications in a smartphone. The experimental study shows that our proposed approach outperforms its counterparts in terms of energy consumption and fast processing of smartphone devices. The battery consumption was reduced to 19% and the data usage was reduced to 16%.

An Effective Context Sensitive Offloading System for Mobile Cloud Environments using Support Value-based Classification

Mobile cloud computing (MCC) has drawn significant research attention recently due to the popularity and capability of portable devices. This paper presents an MCC offloading system based on internet offloading choices. This system guarantees the conservation of battery life and reduced execution time. The proposed effective context sensitive offloading approach using support value-based classification is processed in different steps. Initially, the context data of the input tasks is extracted through the energy consumption model, cost model, execution model, communication model and stored. Then, the support value-based classification approach classifies the tasks based on the context information. This classification creates the information about the tasks and finally, a decision is made at the right time to achieve better offloading. The result indicates the presented offloading framework can choose reasonable cloud assets depending on various contexts of the mobile devices and achieve significant performance enhancement.

Distributed Multi-Dimensional Pricing for Efficient Application Offloading in Mobile Cloud Computing

Offloading computation intensive applications to mobile cloud is promising for overcoming the problems of limited computational resources and energy of mobile devices. However, without considering the competition relationship of mobile users and cloudlets in the mobile cloud computing system, existing studies lack an incentive mechanism for the system to achieve efficient application offloading and cloud resource provisioning. In this paper, we design MPTMG, a Multi-dimensional Pricing mechanism based on Two-sided Market Game. We propose three types of prices: a multi-dimensional price corresponding to multi-dimensional resource allocation, a penalty price to encourage fair and high quality cloud services, and a benefit discount factor to motivate more even provisioning of resources on different dimensions in the cloud. Based on these prices, we propose a distributed price-adjustment algorithm for efficient resource allocation and QoS-aware offloading scheduling. We prove that the algorithm can converge in a finite number of iterations to the equilibrium core allocation at which the mobile cloud system achieves the Pareto efficiency by maximizing the total system benefit. To the best of our knowledge, this is the first paper that applies economic theories and pricing mechanisms to manage application offloading in mobile cloud systems. The simulation results demonstrate that our proposed pricing mechanism can significantly improve the system performance.

Computation Offloading and Scheduling in Edge-Fog Cloud Computing

Resource allocation and task scheduling in the Cloud environment faces many challenges, such as time delay, energy consumption, and security. Also, executing computation tasks of mobile applications on mobile devices (MDs) requires a lot of resources, so they can offload to the Cloud. But Cloud is far from MDs and has challenges as high delay and power consumption. Edge computing with processing near the Internet of Things (IoT) devices have been able to reduce the delay to some extent, but the problem is distancing itself from the Cloud. The fog computing (FC), with the placement of sensors and Cloud, increase the speed and reduce the energy consumption. Thus, FC is suitable for IoT applications. In this article, we review the resource allocation and task scheduling methods in Cloud, Edge and Fog environments, such as traditional, heuristic, and meta-heuristics. We also categorize the researches related to task offloading in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), and Mobile Fog Computing (MFC). Our categorization criteria include the issue, proposed strategy, objectives, framework, and test environment.

Cooperative scheduling of multi‐core and cloud resources: multi‐thread‐based MCC offloading strategy

Modern multi-core mobile devices are the main application objects of mobile cloud computing (MCC). In previous works, researchers have formulated various heuristic algorithms to solve the NP problem. This work combines MCC with multi-threaded computing (MTC) of multi-core mobile devices to avoid NP problems and proposes an MTC-based MCC offloading strategy. First, the authors design an MTC strategy for the application model of cloud computing. Then, they use the data transmission scheme that is dynamically adjusted according to the fading channel state. Finally, based on the MTC strategy and the optimal data transmission scheme, they obtain the MTC-based MCC offloading strategy through a linear time searching algorithm. Simulation results show that compared with the local MTC strategy and the single-threaded MCC offloading strategy, the MTC-based MCC offloading strategy can significantly reduce energy consumption and improve the computing ability in multi-threaded applications.

A survey on computation offloading in the mobile cloud computing environment

Computing intensive tasks could be offloaded from the mobile devices to the remote cloud servers in the Mobile Cloud Computing (MCC) environment. Computing offloading is a complicated problem due to considering the partitioning methods and the migration strategies to achieve the optimal solutions. In recent years, more research works have been done to optimise the problem of computing offloading. However, there are few works that comprehensively review computing offloading in the MCC environment in terms of its models, algorithms and so on. The purpose of this paper is to make a taxonomy for models and algorithms of the computation offloading in MCC environment. A survey is presented to make a classification from four different issues for computation offloading in MCC, and the different approaches taken to tackle these issues are also discussed in detail, further presenting several research challenges in this area.

A survey on computation offloading in the mobile cloud computing environment

Computing intensive tasks could be offloaded from the mobile devices to the remote cloud servers in the Mobile Cloud Computing (MCC) environment. Computing offloading is a complicated problem due to considering the partitioning methods and the migration strategies to achieve the optimal solutions. In recent years, more research works have been done to optimise the problem of computing offloading. However, there are few works that comprehensively review computing offloading in the MCC environment in terms of its models, algorithms and so on. The purpose of this paper is to make a taxonomy for models and algorithms of the computation offloading in MCC environment. A survey is presented to make a classification from four different issues for computation offloading in MCC, and the different approaches taken to tackle these issues are also discussed in detail, further presenting several research challenges in this area.

A context-sensitive offloading system using machine-learning classification algorithms for mobile cloud environment

Computational offloading in Mobile Cloud Computing (MCC) has attracted attention due to benefits in energy saving and improved mobile application performance. Nevertheless, this technique underperforms if the offloading decision ignores contextual information. While recent studies have highlighted the use of contextual information to improve the computational offloading decision, there still remain challenges regarding the dynamic nature of the MCC environment. Most solutions design a single reasoner for the offloading decision and do not know how accurate and precise this technique is, so that when applied in real-world environments it can contribute to inaccurate decisions and consequently the low performance of the overall system. Thus, this paper proposes a Context-Sensitive Offloading System (CSOS) that takes advantage of the main machine-learning reasoning techniques and robust profiling system to provide offloading decisions with high levels of accuracy. We first evaluate the main classification algorithms under our database and the results show that JRIP and J48 classifiers achieves 95% accuracy. Secondly, we develop and evaluate our system under controlled and real scenarios, where context information changes from one experiment to another. Under these conditions, CSOS makes correct decisions as well as ensuring performance gains and energy efficiency.

A quick-response framework for multi-user computation offloading in mobile cloud computing

The execution of much sophisticated applications on the resource-constrained mobile device will lead to the fast exhaustion of the battery of mobile device. Therefore, mobile cloud computing (MCC) is regarded as an energy-effective approach by offloading tasks from mobile device to the resource-enough cloud, which cannot only save energy for mobile devices but also prolong the operation time of battery. However, it still remains a challenging issue to coordinate task offloading among mobile devices and get offloading results quickly at the same time. In this paper, we propose an agent-based MCC framework to enable the device to receive offloading results faster by making offloading decision on the agent. Moreover, to get an offloading strategy, we formulate the problem of maximizing energy savings among multiple users under the completion time and bandwidth constraints. To solve the optimization problem, we propose a Dynamic Programming After Filtering (DPAF) algorithm. In the algorithm, firstly, the original offloading problem is transformed to the classic 0–1 Knapsack problem by the filtering process on the agent. Furthermore, we adopt dynamic programming algorithm to find an optimal offloading strategy. Simulation results show that the framework can more quickly get response from agent than other schemes and the DPAF algorithm outperforms other solutions in energy saving.

Energy and Delay Tradeoff for Application Offloading in Mobile Cloud Computing

Recent work shows that offloading a mobile application from mobile devices to cloud servers can significantly reduce the energy consumption of mobile devices, thus extending the lifetime of mobile devices. However, previous work only considers the energy saving of mobile devices while ignoring the execution delay of mobile applications. To reduce the energy consumption of mobile devices, one may offload as many mobile applications as possible. However, offloading to cloud servers may incur a large execution delay because of the waiting time at the servers or the communication delay from the mobile devices to the servers. Thus, to balance the tradeoff between energy consumption and execution delay of mobile applications, it is necessary to determine whether the mobile application should be offloaded to the cloud server or run locally at the mobile devices. In this paper, we first formulate a joint optimization problem, which minimizes both the energy consumption at the mobile devices and the execution delay of mobile applications. We prove that the proposed problem is NP-hard. For a special case with unlimited residual energy at the mobile device and the same amount of resources required by each mobile application, we present a polynomial-time optimal solution. We also propose an efficient heuristic algorithm to solve the general case of the problem. Finally, simulation results demonstrate the effectiveness of the proposed scheme.

Framework for context-aware computation offloading in mobile cloud computing

Computation offloading is a promising way to improve the performance as well as reducing the battery power consumption of a mobile application by executing some parts of the application on a remote server. Recent researches on mobile cloud computing mainly focus on the code partitioning and offloading techniques, assuming that mobile codes are offloaded to a prepared server. However, the context of a mobile device, such as locations, network conditions and available cloud resources, changes continuously as it moves throughout the day. And applications are also different in computation complexity and coupling degree. So it needs to dynamically select the appropriate cloud resources and offload mobile codes to them on demand, in order to offload in a more effective way. Supporting such capability is not easy for application developers due to (1) adaptability: mobile applications often face changes of runtime environments so that the adaptation on offloading is needed. (2) effectiveness: when the context of the mobile device changes, it needs to decide which cloud resource is used for offloading, and the reduced execution time must be greater than the network delay caused by offloading. This paper proposes a framework, which supports mobile applications with the context-aware computation offloading capability. First, a design pattern is proposed to enable an application to be computation offloaded on-demand. Second, an estimation model is presented to automatically select the cloud resource for offloading. Third, a framework at both client and server sides is implemented to support the design pattern and the estimation model. A thorough evaluation on two real-world applications is proposed, and the results show that our approach can help reduce execution time by 6–96% and power consumption by 60–96% for computation-intensive applications.

A genetic‐based decision algorithm for multisite computation offloading in mobile cloud computing

SummaryMobile cloud computing is a promising approach to improve the mobile device's efficiency in terms of energy consumption and execution time. In this context, mobile devices can offload the computation‐intensive parts of their applications to powerful cloud servers. However, they should decide what computation‐intensive parts are appropriate for offloading to be beneficial instead of local execution on the mobile device. Moreover, in the real world, different types of clouds/servers with heterogeneous processing speeds are available that should be considered for offloading. Because making offloading decision in multisite context is an NP‐complete, obtaining an optimal solution is time consuming. Hence, we use a near optimal decision algorithm to find the best‐possible partitioning for offloading to multisite clouds/servers. We use a genetic algorithm and adjust it for multisite offloading problem. Also, genetic operators are modified to reduce the ineffective solutions and hence obtain the best‐possible solutions in a reasonable time. We evaluated the efficiency of the proposed method using graphs of real mobile applications in simulation experiments. The evaluation results demonstrate that our proposal outperforms other counterparts in terms of energy consumption, execution time, and weighted cost model.

A Heuristic Algorithm for Multi-site Computation Offloading in Mobile Cloud Computing

Due to limitation of mobile device in terms of battery life and processing power, Mobile Cloud Computing (MCC) has become an attractive choice to leverage this shortcoming as the mobile computation could be offloaded to the cloud, which is so-called mobile computation offloading. Existing research on mobile computation offloading considers offloading a mobile computation to a single cloud. However, in the real world a computation service could be provided by multiple clouds and each computation service. Thus, a new and interesting research problem in mobile computation offloading is how to select a computation service for each of the computation tasks of a mobile computation such that the computation time of the mobile computation, the energy consumption of the mobile device and the cost of using the computation services are minimized. This is so called multi-site computation offloading in mobile cloud computing. In this paper we formulate the multi-site computation offloading problem, propose a heuristic algorithm for the multi-site computation offloading problem and evaluate the heuristic algorithm.

ACO-based solution for computation offloading in mobile cloud computing

The cloud computing has attracted growing attentions for its benefits to providing on-demand services, mobile cloud computing (MCC) enables an increasing number of applications and computational services available on mobile devices. In MCC, computation offloading is one of the most important challenges to provide remote execution of applications to the mobile devices. Here we mainly introduce the ant colony optimization (ACO) to address this challeng and propose an ACO-based solution to the computation offloading problem. The proposed method can be well implemented in practice and presents with low computing complexity.

A game-theoretic approach to computation offloading in mobile cloud computing

We consider a three-tier architecture for mobile and pervasive computing scenarios, consisting of a local tier of mobile nodes, a middle tier (cloudlets) of nearby computing nodes, typically located at the mobile nodes access points but characterized by a limited amount of resources, and a remote tier of distant cloud servers, which have practically infinite resources. This architecture has been proposed to get the benefits of computation offloading from mobile nodes to external servers while limiting the use of distant servers whose higher latency could negatively impact the user experience. For this architecture, we consider a usage scenario where no central authority exists and multiple non-cooperative mobile users share the limited computing resources of a close-by cloudlet and can selfishly decide to send their computations to any of the three tiers. We define a model to capture the users interaction and to investigate the effects of computation offloading on the users’ perceived performance. We formulate the problem as a generalized Nash equilibrium problem and show existence of an equilibrium. We present a distributed algorithm for the computation of an equilibrium which is tailored to the problem structure and is based on an in-depth analysis of the underlying equilibrium problem. Through numerical examples, we illustrate its behavior and the characteristics of the achieved equilibria.

Techniques to Minimize State Transfer Costs for Dynamic Execution Offloading in Mobile Cloud Computing

In order to meet the increasing demand for high performance in smartphones, recent studies suggested mobile cloud computing techniques that aim to connect the phones to adjacent powerful cloud servers to throw their computational burden to the servers. These techniques often employ execution offloading schemes that migrate a process between machines during its execution. In execution offloading, code regions to be executed on the server are decided statically or dynamically based on the complex analysis on execution time and process state transfer costs of every region. Expectedly, the transfer cost is a deciding factor for the success of execution offloading. According to our analysis, it is dominated by the total size of heap objects transferred over the network. But previous work did not try hard to minimize this size. Thus in this paper, we introduce novel techniques based on compiler code analysis that effectively reduce the transferred data size by transferring only the essential heap objects and the stack frames actually referenced in the server. The experiments exhibit that the reduced size positively influences not only the transfer time itself but also the overall effectiveness of execution offloading, and ultimately, improves the performance of our mobile cloud computing significantly in terms of execution time and energy consumption.